
"""simple docstring"""


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 					lowerCAmelCase_					(   _lowercase		,       _lowercase     ):



						'''simple docstring'''


						@register_to_config
						def __init__(			self  :   List[str]	,A_  :   int = 128	,A_  :   int = 256	,A_  :   float = 20_00.0	,A_  :   int = 768	,A_  :   int = 12	,A_  :   int = 12	,A_  :   int = 64	,A_  :   int = 2048	,A_  :   float = 0.1	,)  -> str:
							super().__init__()

							A     = nn.Sequential(
							    nn.Linear(A_	,d_model * 4	,bias=A_   )	,nn.SiLU()	,nn.Linear(d_model * 4	,d_model * 4	,bias=A_   )	,nn.SiLU()	,)

							A     = nn.Embedding(A_	,A_   )
							A     = False

							A     = nn.Linear(A_	,A_	,bias=A_   )

							A     = nn.Dropout(p=A_   )

							A     = nn.ModuleList()
							for lyr_num in range(A_   ):
								# FiLM conditional T5 decoder
								A     = DecoderLayer(d_model=A_	,d_kv=A_	,num_heads=A_	,d_ff=A_	,dropout_rate=A_   )
								self.decoders.append(A_   )

							A     = TaLayerNorm(A_   )

							A     = nn.Dropout(p=A_   )
							A     = nn.Linear(A_	,A_	,bias=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   List[Any]	,A_  :   int   )  -> Any:
							A     = torch.mul(query_input.unsqueeze(-1   )	,key_input.unsqueeze(-2   )   )
							return mask.unsqueeze(-3   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Dict	,A_  :   List[Any]	,A_  :   Optional[Any]   )  -> List[Any]:
							A ,   A ,   A     = decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							A     = get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time	,embedding_dim=self.config.d_model	,max_period=self.config.max_decoder_noise_time	,).to(dtype=self.dtype   )

							A     = self.conditioning_emb(A_   ).unsqueeze(1   )

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							A     = decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							A     = torch.broadcast_to(
							    torch.arange(A_	,device=decoder_input_tokens.device   )	,(batch, seq_length)	,)

							A     = self.position_encoding(A_   )

							A     = self.continuous_inputs_projection(A_   )
							inputs += position_encodings
							A     = self.dropout(A_   )

							# decoder: No padding present.
							A     = torch.ones(
							    decoder_input_tokens.shape[:2]	,device=decoder_input_tokens.device	,dtype=inputs.dtype   )

							# Translate encoding masks to encoder-decoder masks.
							A     = [(x, self.encoder_decoder_mask(A_	,A_   )) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							A     = torch.cat([x[0] for x in encodings_and_encdec_masks]	,dim=1   )
							A     = torch.cat([x[1] for x in encodings_and_encdec_masks]	,dim=-1   )

							for lyr in self.decoders:
								A     = lyr(
								    A_	,conditioning_emb=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,)[0]

							A     = self.decoder_norm(A_   )
							A     = self.post_dropout(A_   )

							A     = self.spec_out(A_   )
							return spec_out







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Union[str, Any]	,A_  :   Dict	,A_  :   Optional[int]	,A_  :   Optional[int]	,A_  :   int	,A_  :   Tuple	,A_  :   int=1e-6   )  -> Any:
							super().__init__()
							A     = nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=A_	,d_kv=A_	,num_heads=A_	,dropout_rate=A_   )   )

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=A_	,d_kv=A_	,num_heads=A_	,dropout_rate=A_	,layer_norm_epsilon=A_	,)   )

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=A_	,d_ff=A_	,dropout_rate=A_	,layer_norm_epsilon=A_   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   List[str]	,A_  :   str=None	,A_  :   Any=None	,A_  :   Optional[Any]=None	,A_  :   List[str]=None	,A_  :   Optional[int]=None	,)  -> Optional[int]:
							A     = self.layer[0](
							    A_	,conditioning_emb=A_	,attention_mask=A_	,)

							if encoder_hidden_states is not None:
								A     = torch.where(encoder_attention_mask > 0	,0	,-1e10   ).to(
								    encoder_hidden_states.dtype   )

								A     = self.layer[1](
								    A_	,key_value_states=A_	,attention_mask=A_	,)

							# Apply Film Conditional Feed Forward layer
							A     = self.layer[-1](A_	,A_   )

							return (hidden_states,)







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   int	,A_  :   Optional[int]	,A_  :   str	,A_  :   int	,A_  :   int   )  -> Optional[int]:
							super().__init__()
							A     = TaLayerNorm(A_   )
							A     = TaFiLMLayer(in_features=d_model * 4	,out_features=A_   )
							A     = Attention(query_dim=A_	,heads=A_	,dim_head=A_	,out_bias=A_	,scale_qk=A_   )
							A     = nn.Dropout(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   int	,A_  :   str=None	,A_  :   Optional[Any]=None	,)  -> List[Any]:
							# pre_self_attention_layer_norm
							A     = self.layer_norm(A_   )

							if conditioning_emb is not None:
								A     = self.FiLMLayer(A_	,A_   )

							# Self-attention block
							A     = self.attention(A_   )

							A     = hidden_states + self.dropout(A_   )

							return hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Any	,A_  :   Any	,A_  :   Optional[int]	,A_  :   int	,A_  :   str   )  -> List[str]:
							super().__init__()
							A     = Attention(query_dim=A_	,heads=A_	,dim_head=A_	,out_bias=A_	,scale_qk=A_   )
							A     = TaLayerNorm(A_	,eps=A_   )
							A     = nn.Dropout(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Union[str, Any]	,A_  :   Optional[Any]=None	,A_  :   Tuple=None	,)  -> List[str]:
							A     = self.layer_norm(A_   )
							A     = self.attention(
							    A_	,encoder_hidden_states=A_	,attention_mask=attention_mask.squeeze(1   )	,)
							A     = hidden_states + self.dropout(A_   )
							return layer_output







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Optional[int]	,A_  :   str	,A_  :   str	,A_  :   List[Any]	,A_  :   Tuple   )  -> int:
							super().__init__()
							A     = TaDenseGatedActDense(d_model=A_	,d_ff=A_	,dropout_rate=A_   )
							A     = TaFiLMLayer(in_features=d_model * 4	,out_features=A_   )
							A     = TaLayerNorm(A_	,eps=A_   )
							A     = nn.Dropout(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Optional[int]	,A_  :   Optional[int]=None   )  -> List[str]:
							A     = self.layer_norm(A_   )
							if conditioning_emb is not None:
								A     = self.film(A_	,A_   )

							A     = self.DenseReluDense(A_   )
							A     = hidden_states + self.dropout(A_   )
							return hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Optional[Any]	,A_  :   Tuple	,A_  :   int	,A_  :   str   )  -> Optional[int]:
							super().__init__()
							A     = nn.Linear(A_	,A_	,bias=A_   )
							A     = nn.Linear(A_	,A_	,bias=A_   )
							A     = nn.Linear(A_	,A_	,bias=A_   )
							A     = nn.Dropout(A_   )
							A     = NewGELUActivation()




						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Tuple   )  -> List[str]:
							A     = self.act(self.wi_a(A_   )   )
							A     = self.wi_a(A_   )
							A     = hidden_gelu * hidden_linear
							A     = self.dropout(A_   )

							A     = self.wo(A_   )
							return hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Optional[int]	,A_  :   Tuple=1e-6   )  -> Any:
							super().__init__()
							A     = nn.Parameter(torch.ones(A_   )   )
							A     = eps




						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[str]   )  -> str:
							# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
							# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
							# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
							# half-precision inputs is done in fp32

							A     = hidden_states.to(torch.floataa   ).pow(2   ).mean(-1	,keepdim=A_   )
							A     = hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
								A     = hidden_states.to(self.weight.dtype   )

							return self.weight * hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   torch.Tensor   )  -> torch.Tensor:
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_47_15 * torch.pow(A_	,3.0   ))   ))







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   str	,A_  :   List[Any]	,A_  :   str   )  -> Tuple:
							super().__init__()
							A     = nn.Linear(A_	,out_features * 2	,bias=A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Dict	,A_  :   Tuple   )  -> List[Any]:
							A     = self.scale_bias(A_   )
							A ,   A     = torch.chunk(A_	,2	,-1   )
							A     = x * (1 + scale) + shift
							return x






"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""     )
      lowerCAmelCase    		=			str(bin(SCREAMING_SNAKE_CASE     )     )[2:]  # remove the leading "0b"
      lowerCAmelCase    		=			str(bin(SCREAMING_SNAKE_CASE     )     )[2:]
      lowerCAmelCase    		=			max(len(SCREAMING_SNAKE_CASE     )				,		len(SCREAMING_SNAKE_CASE     )     )
      return "0b" + "".join(
          str(int("""1""" in (char_a, char_b)     )     )
          for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE     )				,		b_binary.zfill(SCREAMING_SNAKE_CASE     )     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






